Animals and experimental design
Female C57BL/6(16-22 g) mice aged 6–8 weeks were purchased from the Model Animal Research Center of Nanjing University. Using Female mice because females have a higher model rate and are easier to model induction . All mice were maintained at the Center for Experimental Animal of Huai’an First People’s Hospital. The rearing environment had a temperature of 22 ± 2℃, and a humidity of 45 ± 10%, with a 12 h light/12 h dark cycle. Animal experiments were approved by the Huai’an First Hospital Laboratory Animal Ethics Committee affiliated with Nanjing Medical University. After one week of acclimation, the mice were randomly divided into three groups of Jatrorrhizine (JA)-treated, DSS, and Control. A dose of 60 mg/kg of Jatrorrhizine was dissolved in 0.2ml of sterile saline and administered to mice by oral gavage. The JA-treated group was given Jatrorrhizine every other day for one week, and the drinking water was replaced with 2.5% DSS (molecular weight, 36–50 kDa; MP Biomedicals, LLC, Irvine, CA, United States) in the second week, and continue to give the same dose by gavage every other day. The mice in the DSS group drank water freely in the first week and were replaced with 2.5% DSS in the second week. Control mice drank normal water for two weeks. From the second week, the body weight, stool property, and hematochezia of the mice were recorded daily, and the disease activity index (DAI)was calculated. On the 14th day of the experiment, mice were euthanized and fresh feces and colon tissues were collected for further analysis.
Disease activity indexes (DAI)
DAI score was graded on a scale of 0–4 based on the following parameters: body weight loss (1, 1–5%; 2, 5–10%; 3, 10–15%; and 4, ≥ 15%), stool consistency (0, normal; 2, loose stools; 4, diarrhea), and blood in the stool (0, no blood seen; 2, apparent blood with stool; 4, grossly bloody stool). Loss of body weight by more than 30% was used as a criterion for humane euthanasia to reduce the pain of the mice. As the DAI score was an indicator of daily assessment and measurement, the researchers were not blinded to the grouping of the experiment.
The distal colon tissue was obtained and fixed in 4% paraformaldehyde for histopathological analysis. Colon tissue samples were dehydrated in gradient alcohol and embedded in paraffin. Sections (4 μm) were stained with hematoxylin and eosin(H&E). The histological score was calculated according to the degrees of inflammation (0, none; 1, mild; 2, moderate; 3, severe), crypt gland damage (0, normal; 1, basal 1/3 damage; 2, basal 2/3 damage; 3, crypt lost and surface epithelium present; 4 crypt and surface epithelium lost), infiltration of lymphocyte (0, 0%; 1, 10%; 2, 10-25%; 3, 25-50%; 4, > 50% ,*The lymphocyte infiltration was observed at 400× magnification), structure of colon wall (0, none; 1, mucosa; 2, submucosa; 3, transmural). The total histological score was expressed as the sum of four parameter scores . The Histological scoring was performed by two independent pathologists who were blind to the experiment condition.
Gut microbiota analysis
Gut microbiota composition was determined by 16 S rRNA gene sequencing. The fresh fecal samples in mice were collected, and the microbial community genomic total DNA was extracted by an E.Z.N.A.® soil DNA Kit (Omega Bio-Tek, Norcross, GA, U.S.). The NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA) was used to check the concentration and purity of DNA. Then, the hypervariable V3-V4 regions of the 16SrRNA sequence were then amplified with primer pairs 338 F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R(5’-GGACTACHVGGGTWTCTAAT-3’) using an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA) . The PCR amplification was carried out for 3 min at 95 ℃ for initial denaturation, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s, and extension at 72 ℃for 45 s, and single extension at 72 ℃ for 10 min, and end at 4 ℃. The PCR products were purified by the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). Sequencing was performed on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA).
16 S rRNA gene sequence analysis
Bioinformatics 16 S rRNA gene sequencing reads were demultiplexed and quality-filtered using QIIME open-source bioinformatics pipeline for an initial assessment . Fastq data were quality controlled by Trimmomatic and Pear. The screening criteria are as follows: (i) a sliding window strategy is adopted using Trimmomatic v0.36, the window size is set to 50 bp, the average quality value is 20, the minimum reserved sequence length is 120, and Pearv0.9.6 is used to remove sequences with N; (ii) Flashv1.20 and Pear0.9.6 are used to merge the sequences at both ends according to the overlapping relationship of PE. The minimum overlap is set to 10 bp and the mismatch rate is 0.1 to obtain the Fasta sequence; (iii) The chimera of the Fasta sequence was removed by chime method according to the known database, and the self-alignment (denovo) method was used to remove the unknown database, and the undesired short sequences were removed at the same time. Operational taxonomic units (OTUs) were clustered with a 97% similarity using UPARSE (version 7.1). The taxonomy of each 16 S rRNA gene sequence was analyzed by Ribosomal Database Project (RDP) Classifier algorithm2 against the Silva (SSU128) 16 S rRNA database .
Calculation of Diversity and Richness Index
Relative abundance (%) was used to represent the relative abundance of OUT and species . The alpha diversity indexes were estimated based on the OUT using Chao 1 and Shannon . Chao1 is an index of the bacterial species, which is used to estimate the number of OTUs in the community. The formula for this index is Schao1 = Sobs + n1(n1-1)/2(n2 + 1), where Sobs = number of observed OTUs, n1 = the number of OTUs with only one sequence, and n2 is the number of OTUs with only two sequences. Shannon is one of the indices used to estimate microbial diversity in a sample. Its formula is as follows: H= -Σ(Pi) (ln Pi), where Pi is the proportion of individuals belonging to species i in the sample. The larger the Shannon value, the higher the community diversity.
Total RNA was extracted from inflamed colon tissue. Samples about 1 cm long were used for sequencing. Then, we collected colon samples from three randomly chosen animals per group for sequencing. Total RNA from colon tissues was isolated with TRIzol reagent. The concentrations of RNA were assessed using a Nanodrop spectrophotometer (IMPLEN, CA, USA). RNA integrity was determined by Agilent 2100 (Agilent Technologies, CA, USA). The sample libraries were generated using NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB, USA) according to the manufacturer’s recommendations. The library was sequenced using Illumina Hiseq 4000 platform and generated paired-end 150 bp reads. We then used Tophat2(v2.1.0)  and Cufflinks (v2.1.1)  software to complete alignment and analyze transcripts, and make a quantitative analysis of all genes. DEGs analysis was performed according to the read count obtained in the gene expression level analysis. For samples with biological replicates, differential expression analysis was performed using DESeq (1.10.1) . The resulting P-values of differential expression analysis were controlled for FDR (false discovery rate) using the method of Benjamini and Hochberg. The standard of differential gene screening is generally: q-value < 0.05. Additionally, Gene Ontology (GO) enrichment analysis was performed using GOseq software  and based on Wallenius non-central hypergeometric distribution. KEGG pathway enrichment analysis was performed using KOBAS software. Rich factor, q-value, and the number of genes enriched in this pathway were used to measure the degree of KEGG enrichment . The rich factor was the value ratio of the number of DEGs in the pathway to all annotated genes enriched in the pathway . A q-value < 0.05 was considered significant enrichment in the KEGG pathways, where the q-value is the adjusted p-value.
Network pharmacology analysis
The active ingredients of Jatrorrhizine were searched through the TCMSP database (http://tcmspw.com/tcmsp.php) and the target protein corresponding to Jatrorrhizine was screened with the oral availability OB ≥ 30% as the limiting condition. Next, the name of the target protein was standardized through the Uniprot (http://www.uniprot.org/) database, the species was set as “Homo Sapiens (human)”, and the confirmed target was used as the screening condition to construct a data set of potential targets of jatrorrhizine. With “ulcerative colitis” as the keyword, the related targets of UC were searched in three databases of the drug target database (Drug-bank), disease databases gene-disease association database (DisGeNET), and the human gene database (GeneCards). After eliminating the repeated disease targets, all the target genes of each database were merged. Using R software (version 4.1.2) to draw the Venn diagram of the predicted targets of jatrorrhizine and the disease targets of ulcerative colitis, and obtain the intersection targets of the two as potential targets of jatrorrhizine in the treatment of ulcerative colitis. Then, the potential targets were imported into the STRING database to obtain the high-confidence protein interaction relationships of intersecting target proteins. Finally, the protein interaction network was drawn with the Cytoscape software, and the target with the largest degree value was selected as the core target of jatrorrhizine in the treatment of UC.
Statistical significance was evaluated using GraphPad Prism 8.0 software. Analysis of variance was used for comparison between groups, and independent samples t-test was used for comparison between two groups. Results are shown as mean ± standard error. Correlation analysis was performed using Pearson correlation analysis in SPSS version 26(IBM SPSS Statistics). All data were normally distributed. P values < 0.05 were considered statistically significant.